SAMI is an AI-powered sales and marketing insights engine with a single query interface that responds to business questions by retrieving information from internal sources like Salesforce.
Corporate data is often siloed, stored across multiple sources, and becomes inaccessible if a designated knowledge owner is unavailable. Third-party commercial AI platforms usually offer limited functionality and customization options, hindering efficient use and failing to deliver the desired outcomes. Meanwhile, for prompt decision-making, it's critical to have all required information available 24/7.
That's why TEAM's AI/Transformation Studio came up with an idea to build our own tool that will improve enterprise AI search systems in environments where off-the-shelf tools struggle, focusing on data quality challenges, system limitations, and evolving architecture.
While creating SAMI, a robust business intelligence chatbot for AI sales enablement, we had to overcome several vital obstacles when it came to combining AI knowledge management and conversational BI.
For SAMI to serve as a company data search tool and answer business questions related to corporate projects, talent, and other areas, it was necessary to capture internal institutional knowledge across information sources such as Salesforce Project, Salesforce Project Assignments, and portfolio case studies.
We followed the Agile approach (Scrum + XP elements), but deviated from typical methodologies to address structural mismatches in Salesforce AI integration. We created our own mix because we worked with AI data retrieval and iterated to achieve truly reliable answering functionality — requiring more than typical Research and Experiments.
The defining capability of our team is taking mere AI solution concepts and transforming them into fully usable products that deliver measurable outcomes in real-world enterprise workflows. The Studio's team thinks in terms of complete systems, not isolated components — closing gaps from parameters not propagating correctly to edge AI cases that only surface in production.
Building an internal AI chatbot with broad conversational BI capabilities requires navigating inherent uncertainty in estimation and delivery, particularly when integrating evolving AI components. Our team continuously adapts to shifting AI capabilities without compromising product stability or usability.
We delivered a fully functional AI sales enablement assistant that responds to user questions about projects, accounts, talent CVs, and portfolio case studies by retrieving information from all eligible internal sources, including Salesforce.
The product leverages sophisticated RAG and is essentially an internal knowledge base AI tool that provides users with a single-query interface to all integrated internal sources, reducing the need to manually search each data location separately. The key differentiator was the techniques we employed to efficiently improve the current model's quality — F1 boosted from <0.3 to >0.6 — and to respond to a wider variety of user prompt types.
The system interprets intent and compensates for imprecise keyword search. When asked about "healthcare projects," it also returns results mentioning "medicine," "patients," "pharmaceuticals," "clinics," and others.
SAMI only returns results that are: a) not hallucinated; and b) relevant to the intent of the prompt.
SAMI can interpret sophisticated, indirect questions, providing competent answers derived with the help of logical reasoning.
Data retrieval time
F1 Score improvement
Single interface for all sources
Without SAMI, it would typically take up to 48 hours to answer questions like "What projects have we done that meet criteria X." The old method: email the question to Delivery Managers, wait for replies, then ask for summaries.
With SAMI, you enter a clear question with desired search parameters and get a quick reply with all the needed information — waiting a couple of minutes instead of 2 days.
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